Given an ordered sample of either exceedances or upper order statistics which is to be modeled using a GPD, this function provides Falk's estimator of the shape parameter γ \in [-1,0]. Precisely,
\hat γ_{\rm{Falk}} = \hat γ_{\rm{Falk}}(k, n) = \frac{1}{k-1} ∑_{j=2}^k \log \Bigl(\frac{X_{(n)}-H^{-1}((n-j+1)/n)}{X_{(n)}-H^{-1}((n-k)/n)} \Bigr), \; \; k=3, … ,n-1
for $H$ either the empirical or the distribution function based on the log–concave density estimator. Note that for any k, \hat γ_{\rm{Falk}} : R^n \to (-∞, 0). If \hat γ_{\rm{Falk}} \not \in [-1,0), then it is likely that the log-concavity assumption is violated.
1 |
est |
Log-concave density estimate based on the sample as output by |
ks |
Indices k at which Falk's estimate should be computed. If set to |
n x 3 matrix with columns: indices k, Falk's estimator based on the log-concave density estimate, and the ordinary Falk's estimator based on the order statistics.
Kaspar Rufibach (maintainer), kaspar.rufibach@gmail.com,
http://www.kasparrufibach.ch
Samuel Mueller, samuel.mueller@sydney.edu.au,
www.maths.usyd.edu.au/ut/people?who=S_Mueller
Kaspar Rufibach acknowledges support by the Swiss National Science Foundation SNF, http://www.snf.ch
Mueller, S. and Rufibach K. (2009). Smooth tail index estimation. J. Stat. Comput. Simul., 79, 1155–1167.
Falk, M. (1995). Some best parameter estimates for distributions with finite endpoint. Statistics, 27, 115–125.
Other approaches to estimate γ based on the fact that the density is log–concave, thus
γ \in [-1,0], are available as the functions pickands
, falkMVUE
, generalizedPick
.
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